We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked objects. In our approach, actions capture both linear, low-level object dynamics, and an additional spatial distribution on where the dynamic occurs. Furthermore, behavior classes capture high-level temporal motion dependencies in Markov chains of actions, thus each learned behavior is a switching linear dynamical system. The number of actions and behaviors is discovered from the data itself using Dirichlet Processes. We are especially interested in cases where tracks can exhibit large kinematic and spatial variations, e.g. person tracks in open environments, as found in the visual surveillance and intelligent vehicle domains. The model handles real-valued features directly, so no information is lost by quantizing measurements into 'visual words', and variations in standing, walking and running can be discovered without discrete thresholds. We describe inference using Markov Chain Monte Carlo sampling and validate our approach on several artificial and real-world pedestrian track datasets from the surveillance and intelligent vehicle domain. We show that our model can distinguish between relevant behavior patterns that an existing state-of-the-art hierarchical model for clustering and simpler model variants cannot. The software and the artificial and surveillance datasets are made publicly available for benchmarking purposes.

This paper presents a smart surveillance system named CASSANDRA, aimed at detecting instances of aggressive human behavior in public environments. A distinguishing aspect of CASSANDRA is the exploitation of complementary audio and video cues to disambiguate scene activity in real-life environments. From the video side, the system uses overlapping cameras to track persons in 3D and to extract features regarding the limb motion relative to the torso. From the audio side, it classifies instances of speech, screaming, singing, and kicking-object. The audio and video cues are fused with contextual cues (interaction, auxiliary objects); a Dynamic Bayesian Network (DBN) produces an estimate of the ambient aggression level. Our prototype system is validated on a realistic set of scenarios performed by professional actors at an actual train station to ensure a realistic audio and video noise setting.

We present a probabilistic framework for the joint estimation of pedestrian head and body orientation from a mobile stereo vision platform. For both head and body parts, we convert the responses of a set of orientation-specific detectors into a (continuous) probability density function. The parts are localized by means of a pictorial structure approach, which balances part-based detector responses with spatial constraints. Head and body orientations are estimated jointly to account for anatomical constraints. The joint single-frame orientation estimates are integrated over time by particle filtering. The experiments involved data from a vehicle-mounted stereo vision camera in a realistic traffic setting; 65 pedestrian tracks were supplied by a state-of-the-art pedestrian tracker. We show that the proposed joint probabilistic orientation estimation framework reduces the mean absolute head and body orientation error up to 15° compared with simpler methods. This results in a mean absolute head/body orientation error of about 21°/19°, which remains fairly constant up to a distance of 25 m. Our system currently runs in near real time (8-9 Hz).

We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method.

Accurate motion models are key to many tasks in the intelligent vehicle domain, but simple Linear Dynamics (e.g. Kalman filtering) do not exploit the spatio-temporal context of motion. We present a method to learn Switching Linear Dynamics of object tracks observed from within a driving vehicle. Each switching state captures object dynamics as a mean motion with variance, but also has an additional spatial distribution on where the dynamic is seen relative to the vehicle. Thus, both an object's previous movements and current location will make certain dynamics more probable for subsequent time steps. To train the model, we use Bayesian inference to sample parameters from the posterior, and jointly learn the required number of dynamics. Unlike Maximum Likelihood learning, inference is robust against overfitting and poor initialization. We demonstrate our approach on an ego-motion compensated track dataset of pedestrians, and illustrate how the switching dynamics can make more accurate path predictions than a mixture of linear dynamics for crossing pedestrians.

We present an approach for the joint probabilistic estimation of pedestrian head and body orientation in the context of intelligent vehicles. For both, head and body, we convert the output of a set of orientation-specific detectors into a full (continuous) probability density function. The parts are localized with a pictorial structure approach which balances part-based detector output with spatial constraints. Head and body orientation estimates are furthermore coupled probabilistically to account for anatomical constraints. Finally, the coupled single-frame orientation estimates are integrated over time by particle filtering. The experiments involve 37 pedestrian tracks obtained from an external stereo vision-based pedestrian detector in realistic traffic settings. We show that the proposed joint probabilistic orientation estimation approach reduces the mean head and body orientation error by 10 degrees and more.

We present a novel non-parametric Bayesian model to jointly discover the dynamics of low-level actions and high-level behaviors of tracked people in open environments. Our model represents behaviors as Markov chains of actions which capture high-level temporal dynamics. Actions may be shared by various behaviors and represent spatially localized occurrences of a person's low-level motion dynamics using Switching Linear Dynamics Systems. Since the model handles real-valued features directly, we do not lose information by quantizing measurements to 'visual words' and can thus discover variations in standing, walking and running without discrete thresholds. We describe inference using Gibbs sampling and validate our approach on several artificial and real-world tracking datasets. We show that our model can distinguish relevant behavior patterns that an existing state-of-the-art method for hierarchical clustering cannot.